A Wireless Body Sensor Network for Activity Monitoring with Low Transmission Overhead

Braojos, Rubén; Beretta, Ivan; Constantin, Joseph; Burg, Andreas; Atienza, David

doi:10.1109/euc.2014.46

Cited by 13 publications

(11 citation statements)

References 15 publications

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“…With the proliferation of portable and mobile devices, and increasing processing capabilities of endpoints in IoT, we envision that the edge of the network is moving to the gateways and smart devices [11,12]. We are witnessing a trend towards processing the captured data at the IoT edge nodes, even when data acquisition rate is relatively high, such as in healthcare monitoring applications which involve biomedical signals [13,14], or structural health monitoring applications [15].…”

Section: Clouds P R O C E S S I N G C a P A B I L I T Y P R E D I C Tmentioning

confidence: 99%

“…Hence, a portion of the processing task must be offloaded to the more powerful layers (e.g. gateways, Fogs or cloudlets) [14,13]. However, to offload the computation, the raw data or partially-processed data must be transmitted to the gateway, and this is where the bandwidth constraint comes into play.…”

Section: Clouds P R O C E S S I N G C a P A B I L I T Y P R E D I C Tmentioning

confidence: 99%

“…It allocates the bandwidth which would result to closest power consumption to p * (Line 10). In cases that b * and b + are close, the gateway increases the allocated bandwidth of d * by Δ, which represents the minimum amount of bandwidth that can be given to or taken away from an IoT device (Lines [13][14]. It iteratively keeps repeating this procedure until the remaining bandwidth, R is totally consumed.…”

Section: B Bandwidth Allocation and Offloading Managementmentioning

confidence: 99%

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Computation offloading and resource allocation for low-power IoT edge devices

Samie

Tsoutsouras

Bauer

et al. 2016

2016 IEEE 3rd World Forum on Internet of Things (WF-IoT)

116

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With the proliferation of portable and mobile IoT devices and their increasing processing capability, we witness that the edge of network is moving to the IoT gateways and smart devices. To avoid Big Data issues (e.g. high latency of cloud based IoT), the processing of the captured data is starting from the IoT edge node. However, the available processing capabilities and energy resources are still limited and do not allow to fully process the data on-board. It calls for offloading some portions of computation to the gateway or servers. Due to the limited bandwidth of the IoT gateways, choosing the offloading levels of connected devices and allocating bandwidth to them is a challenging problem. This paper proposes a technique for managing computation offloading in a local IoT network under bandwidth constraints. The existing bandwidth allocation and computation offloading management techniques underutilize the gateway's resources (e.g. bandwidth) due to the fragmentation issue. This issue stems from the discrete coarse-grained choices (i.e. offloading levels) on the IoT end nodes. Our proposed technique addresses this issue, and utilizes the available resources of the gateway effectively. The experimental results show on average 1 hour (up to 1.5 hour) improvement in battery life of edge devices. The utilization of gateway's bandwidth increased by 40%. 1

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Section: Clouds P R O C E S S I N G C a P A B I L I T Y P R E D I C Tmentioning

confidence: 99%

Section: Clouds P R O C E S S I N G C a P A B I L I T Y P R E D I C Tmentioning

confidence: 99%

Section: B Bandwidth Allocation and Offloading Managementmentioning

confidence: 99%

See 1 more Smart Citation

Computation offloading and resource allocation for low-power IoT edge devices

Samie

Tsoutsouras

Bauer

et al. 2016

2016 IEEE 3rd World Forum on Internet of Things (WF-IoT)

116

View full text Add to dashboard Cite

show abstract

“…Due to the first constraint, some of the IoT applications are not able to autonomously process the entire collected data on the IoT devices. Hence, a portion of the processing task is offloaded to more powerful layers (e.g., gateways) (Bortolotti et al 2016;Braojos et al 2014). However, to offload the computation, the raw or partially/processed data must be transmitted to the gateway, and this is where the bandwidth constraint comes into play.…”

Section: Introductionmentioning

confidence: 99%

Distributed Trade-Based Edge Device Management in Multi-Gateway IoT

Samie

Tsoutsouras

Bauer

et al. 2018

ACM Trans. Cyber-Phys. Syst.

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The Internet-of-Things (IoT) envisions an infrastructure of ubiquitous networked smart devices offering advanced monitoring and control services. The current art in IoT architectures utilizes gateways to enable application-specific connectivity to IoT devices. In typical configurations, IoT gateways are shared among several IoT edge devices. Given the limited available bandwidth and processing capabilities of an IoT gateway, the service quality (SQ) of connected IoT edge devices must be adjusted over time not only to fulfill the needs of individual IoT device users but also to tolerate the SQ needs of the other IoT edge devices sharing the same gateway. However, having multiple gateways introduces an interdependent problem, the binding, i.e., which IoT device shall connect to which gateway. In this article, we jointly address the binding and allocation problems of IoT edge devices in a multigateway system under the constraints of available bandwidth, processing power, and battery lifetime. We propose a distributed trade-based mechanism in which after an initial setup, gateways negotiate and trade the IoT edge devices to increase the overall SQ. We evaluate the efficiency of the proposed approach with a case study and through extensive experimentation over different IoT system configurations regarding the number and type of the employed IoT edge devices. Experiments show that our solution improves the overall SQ by up to 56% compared to an unsupervised system. Our solution also achieves up to 24.6% improvement on overall SQ compared to the state-of-the-art SQ management scheme, while they both meet the battery lifetime constraints of the IoT devices.

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“…With the expanding number of hindrances between the nodes [13], we can watch more packet loss and more latency that will cause a disconnected communication between the patient and the system. In this way, we need to expand the quantity of relay nodes inside the perception conditions, and improve the dependability for remote availability [14]. As well, an emergency alert service utilizing short message service (SMS) messaging is added to the proposed framework for emergency rescues and responses [16]- [17].…”

Section: Introductionmentioning

confidence: 99%

Efficient EER-LEACH Protocol for Monitoring the Activities of Pregnant Women Using Wearable Body Sensor Network

Subalakshmi¹,

Murugaboopathi²,

Senthilkumar³

2018

IJET

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In health care monitoring framework, it is important to always screen the patient's physiological parameters. For instance, pregnant lady parameters, like, blood pressure, uterine contraction heart rate of pregnant ladies and heart rate alongside the fetal development to detect their wellbeing condition. This paper introduces a monitoring framework that has the capacity to screen physiological changes from the pregnant ladies. In the proposed framework, controller node (CN) has connected on patient body to gather every one of the signs from the uterine contraction monitoring wearable body sensors and sends them to the base station. The appended sensors on patient's body frame a wireless body sensor network (WBSN) and they can detect the uterine constriction, heart rate, blood pressure etc. This framework can recognize the unusual conditions, issue an alert to the patient and send to the doctor. Likewise, the proposed framework comprises of a few relay nodes which are in charge of handing-off the information sent by the controller node (CN) and forward them to the base station. The principle effort of this framework is to diminish the vitality utilization to delay the system lifetime, accelerate and stretch out anticipated work is to expand the sensor execution. This framework is likewise created for multi-patient observing for hospital healthcare and contrasted with the current systems in view of coverage, energy utilization and speed.

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A Wireless Body Sensor Network for Activity Monitoring with Low Transmission Overhead

Cited by 13 publications

References 15 publications

Computation offloading and resource allocation for low-power IoT edge devices

Computation offloading and resource allocation for low-power IoT edge devices

Distributed Trade-Based Edge Device Management in Multi-Gateway IoT

Efficient EER-LEACH Protocol for Monitoring the Activities of Pregnant Women Using Wearable Body Sensor Network

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